import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report 
import os

print (os.getcwd())

c:\Users\ASUS\OneDrive\Desktop\PY_Jupyter

os.chdir ('C:\\Users\\ASUS\\Downloads')
print (os.getcwd())

C:\Users\ASUS\Downloads

df=pd.read_csv('parkinsons.data')
display(df)

display (df.shape)

(195, 24)

print (len(df))
display (df.dtypes ) 
print (df.info()) 
display (df.describe()) 
display (df.isna().sum() )
print (df.columns)

195

name                 object
MDVP:Fo(Hz)         float64
MDVP:Fhi(Hz)        float64
MDVP:Flo(Hz)        float64
MDVP:Jitter(%)      float64
MDVP:Jitter(Abs)    float64
MDVP:RAP            float64
MDVP:PPQ            float64
Jitter:DDP          float64
MDVP:Shimmer        float64
MDVP:Shimmer(dB)    float64
Shimmer:APQ3        float64
Shimmer:APQ5        float64
MDVP:APQ            float64
Shimmer:DDA         float64
NHR                 float64
HNR                 float64
status                int64
RPDE                float64
DFA                 float64
spread1             float64
spread2             float64
D2                  float64
PPE                 float64
dtype: object

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 195 entries, 0 to 194
Data columns (total 24 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   name              195 non-null    object 
 1   MDVP:Fo(Hz)       195 non-null    float64
 2   MDVP:Fhi(Hz)      195 non-null    float64
 3   MDVP:Flo(Hz)      195 non-null    float64
 4   MDVP:Jitter(%)    195 non-null    float64
 5   MDVP:Jitter(Abs)  195 non-null    float64
 6   MDVP:RAP          195 non-null    float64
 7   MDVP:PPQ          195 non-null    float64
 8   Jitter:DDP        195 non-null    float64
 9   MDVP:Shimmer      195 non-null    float64
 10  MDVP:Shimmer(dB)  195 non-null    float64
 11  Shimmer:APQ3      195 non-null    float64
 12  Shimmer:APQ5      195 non-null    float64
 13  MDVP:APQ          195 non-null    float64
 14  Shimmer:DDA       195 non-null    float64
 15  NHR               195 non-null    float64
 16  HNR               195 non-null    float64
 17  status            195 non-null    int64  
 18  RPDE              195 non-null    float64
 19  DFA               195 non-null    float64
 20  spread1           195 non-null    float64
 21  spread2           195 non-null    float64
 22  D2                195 non-null    float64
 23  PPE               195 non-null    float64
dtypes: float64(22), int64(1), object(1)
memory usage: 36.7+ KB
None

name                0
MDVP:Fo(Hz)         0
MDVP:Fhi(Hz)        0
MDVP:Flo(Hz)        0
MDVP:Jitter(%)      0
MDVP:Jitter(Abs)    0
MDVP:RAP            0
MDVP:PPQ            0
Jitter:DDP          0
MDVP:Shimmer        0
MDVP:Shimmer(dB)    0
Shimmer:APQ3        0
Shimmer:APQ5        0
MDVP:APQ            0
Shimmer:DDA         0
NHR                 0
HNR                 0
status              0
RPDE                0
DFA                 0
spread1             0
spread2             0
D2                  0
PPE                 0
dtype: int64

Index(['name', 'MDVP:Fo(Hz)', 'MDVP:Fhi(Hz)', 'MDVP:Flo(Hz)', 'MDVP:Jitter(%)',
       'MDVP:Jitter(Abs)', 'MDVP:RAP', 'MDVP:PPQ', 'Jitter:DDP',
       'MDVP:Shimmer', 'MDVP:Shimmer(dB)', 'Shimmer:APQ3', 'Shimmer:APQ5',
       'MDVP:APQ', 'Shimmer:DDA', 'NHR', 'HNR', 'status', 'RPDE', 'DFA',
       'spread1', 'spread2', 'D2', 'PPE'],
      dtype='object')

import matplotlib.pyplot as plt
plt.figure(figsize=(10, 6))
df.status.hist()
plt.xlabel('Status')
plt.ylabel('Frequencies')
plt.plot()
plt.show()

plt.figure(figsize=(10, 6))
sns.barplot(x="status",y="NHR",data=df);
plt.show()

plt.figure(figsize=(10, 6))
sns.barplot(x="status",y="HNR",data=df);
plt.show()

plt.figure(figsize=(10, 6))
sns.barplot(x="status",y="RPDE",data=df);
plt.show()

import warnings
warnings.filterwarnings('ignore')
rows=3
cols=7
fig, ax=plt.subplots(nrows=rows,ncols=cols,figsize=(16,4))
col=df.columns
index=1
for i in range(rows):
    for j in range(cols):
        sns.distplot(df[col[index]],ax=ax[i][j])
        index=index+1
        
plt.tight_layout()
plt.show()

corr=df.corr
display (df.corr)

<bound method DataFrame.corr of                name  MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \
0    phon_R01_S01_1      119.992       157.302        74.997         0.00784   
1    phon_R01_S01_2      122.400       148.650       113.819         0.00968   
2    phon_R01_S01_3      116.682       131.111       111.555         0.01050   
3    phon_R01_S01_4      116.676       137.871       111.366         0.00997   
4    phon_R01_S01_5      116.014       141.781       110.655         0.01284   
..              ...          ...           ...           ...             ...   
190  phon_R01_S50_2      174.188       230.978        94.261         0.00459   
191  phon_R01_S50_3      209.516       253.017        89.488         0.00564   
192  phon_R01_S50_4      174.688       240.005        74.287         0.01360   
193  phon_R01_S50_5      198.764       396.961        74.904         0.00740   
194  phon_R01_S50_6      214.289       260.277        77.973         0.00567   

     MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  ...  \
0             0.00007   0.00370   0.00554     0.01109       0.04374  ...   
1             0.00008   0.00465   0.00696     0.01394       0.06134  ...   
2             0.00009   0.00544   0.00781     0.01633       0.05233  ...   
3             0.00009   0.00502   0.00698     0.01505       0.05492  ...   
4             0.00011   0.00655   0.00908     0.01966       0.06425  ...   
..                ...       ...       ...         ...           ...  ...   
190           0.00003   0.00263   0.00259     0.00790       0.04087  ...   
191           0.00003   0.00331   0.00292     0.00994       0.02751  ...   
192           0.00008   0.00624   0.00564     0.01873       0.02308  ...   
193           0.00004   0.00370   0.00390     0.01109       0.02296  ...   
194           0.00003   0.00295   0.00317     0.00885       0.01884  ...   

     Shimmer:DDA      NHR     HNR  status      RPDE       DFA   spread1  \
0        0.06545  0.02211  21.033       1  0.414783  0.815285 -4.813031   
1        0.09403  0.01929  19.085       1  0.458359  0.819521 -4.075192   
2        0.08270  0.01309  20.651       1  0.429895  0.825288 -4.443179   
3        0.08771  0.01353  20.644       1  0.434969  0.819235 -4.117501   
4        0.10470  0.01767  19.649       1  0.417356  0.823484 -3.747787   
..           ...      ...     ...     ...       ...       ...       ...   
190      0.07008  0.02764  19.517       0  0.448439  0.657899 -6.538586   
191      0.04812  0.01810  19.147       0  0.431674  0.683244 -6.195325   
192      0.03804  0.10715  17.883       0  0.407567  0.655683 -6.787197   
193      0.03794  0.07223  19.020       0  0.451221  0.643956 -6.744577   
194      0.03078  0.04398  21.209       0  0.462803  0.664357 -5.724056   

      spread2        D2       PPE  
0    0.266482  2.301442  0.284654  
1    0.335590  2.486855  0.368674  
2    0.311173  2.342259  0.332634  
3    0.334147  2.405554  0.368975  
4    0.234513  2.332180  0.410335  
..        ...       ...       ...  
190  0.121952  2.657476  0.133050  
191  0.129303  2.784312  0.168895  
192  0.158453  2.679772  0.131728  
193  0.207454  2.138608  0.123306  
194  0.190667  2.555477  0.148569  

[195 rows x 24 columns]>

from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 20,10

sns.heatmap(df.corr(), xticklabels=True, yticklabels=False, cmap='cubehelix',annot = True)
plt.show()

df.drop(['name'],axis=1,inplace=True)
display (df)

X=df.drop(labels=['status'],axis=1)
display (X.head())

Y=df['status']
display (Y.head())

0    1
1    1
2    1
3    1
4    1
Name: status, dtype: int64

X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2,random_state=40)
print (X.shape,Y.shape)
print(X_train.shape,X_test.shape,Y_train.shape,Y_test.shape)

(195, 22) (195,)
(156, 22) (39, 22) (156,) (39,)

#Create a Logistic Regression Model 
log_reg = LogisticRegression().fit(X_train, Y_train)
#predict on train 
train_preds = log_reg.predict(X_train)

#accuracy on train
print("Model accuracy on train is: ", accuracy_score(Y_train, train_preds))

Model accuracy on train is:  0.8782051282051282

#predict on test
test_preds = log_reg.predict(X_test)

#accuracy on test
print("Model accuracy on test is: ", accuracy_score(Y_test, test_preds))
print('-'*50)

Model accuracy on test is:  0.8717948717948718
--------------------------------------------------

print("confusion_matrix train is:\n ", confusion_matrix(Y_train, train_preds))
print("confusion_matrix test is:\n ", confusion_matrix(Y_test, test_preds))
print('\nClassification Report Train is ')
print(classification_report (Y_train, train_preds))
print('\nClassification Report Test is ')
print(classification_report (Y_test, test_preds))

confusion_matrix train is:
  [[ 25  15]
 [  4 112]]
confusion_matrix test is:
  [[ 5  3]
 [ 2 29]]

Classification Report Train is 
              precision    recall  f1-score   support

           0       0.86      0.62      0.72        40
           1       0.88      0.97      0.92       116

    accuracy                           0.88       156
   macro avg       0.87      0.80      0.82       156
weighted avg       0.88      0.88      0.87       156


Classification Report Test is 
              precision    recall  f1-score   support

           0       0.71      0.62      0.67         8
           1       0.91      0.94      0.92        31

    accuracy                           0.87        39
   macro avg       0.81      0.78      0.79        39
weighted avg       0.87      0.87      0.87        39

#Create Random Forest Model 

RF=RandomForestClassifier().fit(X_train,Y_train)

#predict on train 
train_preds2 = RF.predict(X_train)
#accuracy on train
print("Model accuracy on train is: ", accuracy_score(Y_train, train_preds2))

Model accuracy on train is:  1.0

#predict on test
test_preds2 = RF.predict(X_test)
#accuracy on test
print("Model accuracy on test is: ", accuracy_score(Y_test, test_preds2))

Model accuracy on test is:  0.8717948717948718

#Confusion matrix
print("confusion_matrix train is:\n ", confusion_matrix(Y_train, train_preds2))
print("confusion_matrix test is:\n ", confusion_matrix(Y_test, test_preds2))
print('\nClassification Report Train is ')
print(classification_report (Y_train, train_preds2))
print('\nClassification Report Test is ')
print(classification_report (Y_test, test_preds2))

confusion_matrix train is:
  [[ 40   0]
 [  0 116]]
confusion_matrix test is:
  [[ 5  3]
 [ 2 29]]

Classification Report Train is 
              precision    recall  f1-score   support

           0       1.00      1.00      1.00        40
           1       1.00      1.00      1.00       116

    accuracy                           1.00       156
   macro avg       1.00      1.00      1.00       156
weighted avg       1.00      1.00      1.00       156


Classification Report Test is 
              precision    recall  f1-score   support

           0       0.71      0.62      0.67         8
           1       0.91      0.94      0.92        31

    accuracy                           0.87        39
   macro avg       0.81      0.78      0.79        39
weighted avg       0.87      0.87      0.87        39

print((Y_test !=test_preds2).sum(),'/',((Y_test == test_preds2).sum()+(Y_test != test_preds2).sum()))

5 / 39

print('KappaScore is: ', metrics.cohen_kappa_score(Y_test,test_preds2))

KappaScore is:  0.587737843551797

ddf=pd.DataFrame(data=[test_preds2,Y_test])
display (ddf)

display (ddf.T)

from sklearn.tree import DecisionTreeClassifier
#fit the model on train data 
DT = DecisionTreeClassifier().fit(X,Y)

#predict on train 
train_preds3 = DT.predict(X_train)
#accuracy on train
print("Model accuracy on train is: ", accuracy_score(Y_train, train_preds3))

Model accuracy on train is:  1.0

#predict on test
test_preds3 = DT.predict(X_test)
#accuracy on test
print("Model accuracy on test is: ", accuracy_score(Y_test, test_preds3))
print('-'*50)

Model accuracy on test is:  1.0
--------------------------------------------------

#Confusion matrix
print("confusion_matrix train is:\n ", confusion_matrix(Y_train, train_preds3))
print("confusion_matrix test is: \n", confusion_matrix(Y_test, test_preds3))
print('Wrong predictions out of total')
print('-'*50)
print('\nClassification Report Train is ')
print(classification_report (Y_train, train_preds3))
print('\nClassification Report Test is ')
print(classification_report (Y_test, test_preds3))

confusion_matrix train is:
  [[ 40   0]
 [  0 116]]
confusion_matrix test is: 
 [[ 8  0]
 [ 0 31]]
Wrong predictions out of total
--------------------------------------------------

Classification Report Train is 
              precision    recall  f1-score   support

           0       1.00      1.00      1.00        40
           1       1.00      1.00      1.00       116

    accuracy                           1.00       156
   macro avg       1.00      1.00      1.00       156
weighted avg       1.00      1.00      1.00       156


Classification Report Test is 
              precision    recall  f1-score   support

           0       1.00      1.00      1.00         8
           1       1.00      1.00      1.00        31

    accuracy                           1.00        39
   macro avg       1.00      1.00      1.00        39
weighted avg       1.00      1.00      1.00        39

# Wrong Predictions made.
print((Y_test !=test_preds3).sum(),'/',((Y_test == test_preds3).sum()+(Y_test != test_preds3).sum()))
print('-'*50)

0 / 39
--------------------------------------------------

#kappa score
print('KappaScore is: ', metrics.cohen_kappa_score(Y_test,test_preds3))

KappaScore is:  1.0

from sklearn.naive_bayes import GaussianNB
#fit the model on train data 
NB=GaussianNB()
NB.fit(X_train,Y_train)
#predict on train 
train_preds4 = NB.predict(X_train)
#accuracy on train
print("Model accuracy on train is: ", accuracy_score(Y_train, train_preds4))

#predict on test
test_preds4 = NB.predict(X_test)
#accuracy on test
print("Model accuracy on test is: ", accuracy_score(Y_test, test_preds4))
print('-'*50)
#Confusion matrix
print("confusion_matrix train is: \n", confusion_matrix(Y_train, train_preds4))
print("confusion_matrix test is:\n ", confusion_matrix(Y_test, test_preds4))
print('Wrong predictions out of total')
print('-'*50)
print('\nClassification Report Train is ')
print(classification_report (Y_train, train_preds4))
print('\nClassification Report Test is ')
print(classification_report (Y_test, test_preds4))

Model accuracy on train is:  0.7307692307692307
Model accuracy on test is:  0.6923076923076923
--------------------------------------------------
confusion_matrix train is: 
 [[38  2]
 [40 76]]
confusion_matrix test is:
  [[ 8  0]
 [12 19]]
Wrong predictions out of total
--------------------------------------------------

Classification Report Train is 
              precision    recall  f1-score   support

           0       0.49      0.95      0.64        40
           1       0.97      0.66      0.78       116

    accuracy                           0.73       156
   macro avg       0.73      0.80      0.71       156
weighted avg       0.85      0.73      0.75       156


Classification Report Test is 
              precision    recall  f1-score   support

           0       0.40      1.00      0.57         8
           1       1.00      0.61      0.76        31

    accuracy                           0.69        39
   macro avg       0.70      0.81      0.67        39
weighted avg       0.88      0.69      0.72        39

# Wrong Predictions made.

print((Y_test !=test_preds4).sum(),'/',((Y_test == test_preds4).sum()+(Y_test != test_preds4).sum()))
print('-'*50)

12 / 39
--------------------------------------------------

# Kappa Score
print('KappaScore is: ', metrics.cohen_kappa_score(Y_test,test_preds4))

KappaScore is:  0.3937823834196892

from sklearn.neighbors import KNeighborsClassifier
#fit the model on train data 
KNN = KNeighborsClassifier().fit(X_train,Y_train)
#predict on train 
train_preds5 = KNN.predict(X_train)
#accuracy on train
print("Model accuracy on train is: ", accuracy_score(Y_train, train_preds5))

Model accuracy on train is:  0.9102564102564102

#predict on test
test_preds5 = KNN.predict(X_test)
#accuracy on test
print("Model accuracy on test is: ", accuracy_score(Y_test, test_preds5))
print('-'*50)
#Confusion matrix
print("confusion_matrix train is:\n ", confusion_matrix(Y_train, train_preds5))
print("confusion_matrix test is:\n ", confusion_matrix(Y_test, test_preds5))
print('Wrong predictions out of total')
print('-'*50)
print('\nClassification Report Train is ')
print(classification_report (Y_train, train_preds5))
print('\nClassification Report Test is ')
print(classification_report (Y_test, test_preds5))

Model accuracy on test is:  0.8461538461538461
--------------------------------------------------
confusion_matrix train is:
  [[ 30  10]
 [  4 112]]
confusion_matrix test is:
  [[ 4  4]
 [ 2 29]]
Wrong predictions out of total
--------------------------------------------------

Classification Report Train is 
              precision    recall  f1-score   support

           0       0.88      0.75      0.81        40
           1       0.92      0.97      0.94       116

    accuracy                           0.91       156
   macro avg       0.90      0.86      0.88       156
weighted avg       0.91      0.91      0.91       156


Classification Report Test is 
              precision    recall  f1-score   support

           0       0.67      0.50      0.57         8
           1       0.88      0.94      0.91        31

    accuracy                           0.85        39
   macro avg       0.77      0.72      0.74        39
weighted avg       0.84      0.85      0.84        39

# Wrong Predictions made.
print((Y_test !=test_preds5).sum(),'/',((Y_test == test_preds5).sum()+(Y_test != test_preds5).sum()))

6 / 39

# Kappa Score
print('KappaScore is: ', metrics.cohen_kappa_score(Y_test,test_preds5))

KappaScore is:  0.48

from sklearn.svm import SVC
#fit the model on train data 
SVM = SVC(kernel='linear')
SVM.fit(X_train, Y_train)

#predict on train 
train_preds6 = SVM.predict(X_train)
#accuracy on train
print("Model accuracy on train is: ", accuracy_score(Y_train, train_preds6))

Model accuracy on train is:  0.8782051282051282

#predict on test
test_preds6 = SVM.predict(X_test)
#accuracy on test
print("Model accuracy on test is: ", accuracy_score(Y_test, test_preds6))

Model accuracy on test is:  0.8974358974358975

#Confusion matrix
print("confusion_matrix train is: \n", confusion_matrix(Y_train, train_preds6))
print("confusion_matrix test is:\n ", confusion_matrix(Y_test, test_preds6))
print('Wrong predictions out of total')
print('-'*50)

print("recall", metrics.recall_score(Y_test, test_preds6))
print('-'*50)
print('\nClassification Report Train is ')
print(classification_report (Y_train, train_preds6))
print('\nClassification Report Test is ')
print(classification_report (Y_test, test_preds6))

confusion_matrix train is: 
 [[ 23  17]
 [  2 114]]
confusion_matrix test is:
  [[ 5  3]
 [ 1 30]]
Wrong predictions out of total
--------------------------------------------------
recall 0.967741935483871
--------------------------------------------------

Classification Report Train is 
              precision    recall  f1-score   support

           0       0.92      0.57      0.71        40
           1       0.87      0.98      0.92       116

    accuracy                           0.88       156
   macro avg       0.90      0.78      0.82       156
weighted avg       0.88      0.88      0.87       156


Classification Report Test is 
              precision    recall  f1-score   support

           0       0.83      0.62      0.71         8
           1       0.91      0.97      0.94        31

    accuracy                           0.90        39
   macro avg       0.87      0.80      0.83        39
weighted avg       0.89      0.90      0.89        39

# Wrong Predictions made.
print((Y_test !=test_preds6).sum(),'/',((Y_test == test_preds6).sum()+(Y_test != test_preds6).sum()))
print('-'*50)
# Kappa Score
print('KappaScore is: ', metrics.cohen_kappa_score(Y_test,test_preds6))

4 / 39
--------------------------------------------------
KappaScore is:  0.6533333333333333

import pickle 
# Saving model to disk
pickle.dump(SVM,open('deploy_SVM.pkl','wb'))
# Open the Pickle File 
model=pickle.load(open('deploy_SVM.pkl','rb'))
# Prediction 
print (model.predict (X_train))

[1 1 1 1 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 0 0 1 1 1
 0 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1
 1 1 1 1 1 1 1 0 1 1 1 1 0 1 0 0 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 0 1 1]

import ydata_profiling as pf
display(pf.ProfileReport(df))

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]

Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]

Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]

	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	MDVP:Shimmer(dB)	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
count	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	...	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000
mean	154.228641	197.104918	116.324631	0.006220	0.000044	0.003306	0.003446	0.009920	0.029709	0.282251	...	0.046993	0.024847	21.885974	0.753846	0.498536	0.718099	-5.684397	0.226510	2.381826	0.206552
std	41.390065	91.491548	43.521413	0.004848	0.000035	0.002968	0.002759	0.008903	0.018857	0.194877	...	0.030459	0.040418	4.425764	0.431878	0.103942	0.055336	1.090208	0.083406	0.382799	0.090119
min	88.333000	102.145000	65.476000	0.001680	0.000007	0.000680	0.000920	0.002040	0.009540	0.085000	...	0.013640	0.000650	8.441000	0.000000	0.256570	0.574282	-7.964984	0.006274	1.423287	0.044539
25%	117.572000	134.862500	84.291000	0.003460	0.000020	0.001660	0.001860	0.004985	0.016505	0.148500	...	0.024735	0.005925	19.198000	1.000000	0.421306	0.674758	-6.450096	0.174351	2.099125	0.137451
50%	148.790000	175.829000	104.315000	0.004940	0.000030	0.002500	0.002690	0.007490	0.022970	0.221000	...	0.038360	0.011660	22.085000	1.000000	0.495954	0.722254	-5.720868	0.218885	2.361532	0.194052
75%	182.769000	224.205500	140.018500	0.007365	0.000060	0.003835	0.003955	0.011505	0.037885	0.350000	...	0.060795	0.025640	25.075500	1.000000	0.587562	0.761881	-5.046192	0.279234	2.636456	0.252980
max	260.105000	592.030000	239.170000	0.033160	0.000260	0.021440	0.019580	0.064330	0.119080	1.302000	...	0.169420	0.314820	33.047000	1.000000	0.685151	0.825288	-2.434031	0.450493	3.671155	0.527367

	name	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
0	phon_R01_S01_1	119.992	157.302	74.997	0.00784	0.00007	0.00370	0.00554	0.01109	0.04374	...	0.06545	0.02211	21.033	1	0.414783	0.815285	-4.813031	0.266482	2.301442	0.284654
1	phon_R01_S01_2	122.400	148.650	113.819	0.00968	0.00008	0.00465	0.00696	0.01394	0.06134	...	0.09403	0.01929	19.085	1	0.458359	0.819521	-4.075192	0.335590	2.486855	0.368674
2	phon_R01_S01_3	116.682	131.111	111.555	0.01050	0.00009	0.00544	0.00781	0.01633	0.05233	...	0.08270	0.01309	20.651	1	0.429895	0.825288	-4.443179	0.311173	2.342259	0.332634
3	phon_R01_S01_4	116.676	137.871	111.366	0.00997	0.00009	0.00502	0.00698	0.01505	0.05492	...	0.08771	0.01353	20.644	1	0.434969	0.819235	-4.117501	0.334147	2.405554	0.368975
4	phon_R01_S01_5	116.014	141.781	110.655	0.01284	0.00011	0.00655	0.00908	0.01966	0.06425	...	0.10470	0.01767	19.649	1	0.417356	0.823484	-3.747787	0.234513	2.332180	0.410335
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
190	phon_R01_S50_2	174.188	230.978	94.261	0.00459	0.00003	0.00263	0.00259	0.00790	0.04087	...	0.07008	0.02764	19.517	0	0.448439	0.657899	-6.538586	0.121952	2.657476	0.133050
191	phon_R01_S50_3	209.516	253.017	89.488	0.00564	0.00003	0.00331	0.00292	0.00994	0.02751	...	0.04812	0.01810	19.147	0	0.431674	0.683244	-6.195325	0.129303	2.784312	0.168895
192	phon_R01_S50_4	174.688	240.005	74.287	0.01360	0.00008	0.00624	0.00564	0.01873	0.02308	...	0.03804	0.10715	17.883	0	0.407567	0.655683	-6.787197	0.158453	2.679772	0.131728
193	phon_R01_S50_5	198.764	396.961	74.904	0.00740	0.00004	0.00370	0.00390	0.01109	0.02296	...	0.03794	0.07223	19.020	0	0.451221	0.643956	-6.744577	0.207454	2.138608	0.123306
194	phon_R01_S50_6	214.289	260.277	77.973	0.00567	0.00003	0.00295	0.00317	0.00885	0.01884	...	0.03078	0.04398	21.209	0	0.462803	0.664357	-5.724056	0.190667	2.555477	0.148569

	0	1
0	1	1
1	1	1
2	1	1
3	0	0
4	1	1
5	1	0
6	1	1
7	1	1
8	1	1
9	1	0
10	1	1
11	1	1
12	1	0
13	1	1
14	1	1
15	1	1
16	1	1
17	1	1
18	1	1
19	1	1
20	1	1
21	0	1
22	1	1
23	1	1
24	0	0
25	1	1
26	1	1
27	1	1
28	1	1
29	1	1
30	0	0
31	0	0
32	0	0
33	1	1
34	1	1
35	1	1
36	1	1
37	0	1
38	1	1

	0	1
0	1	1
1	1	1
2	1	1
3	0	0
4	1	1
5	1	0
6	1	1
7	1	1
8	1	1
9	1	0
10	1	1
11	1	1
12	1	0
13	1	1
14	1	1
15	1	1
16	1	1
17	1	1
18	1	1
19	1	1
20	1	1
21	0	1
22	1	1
23	1	1
24	0	0
25	1	1
26	1	1
27	1	1
28	1	1
29	1	1
30	0	0
31	0	0
32	0	0
33	1	1
34	1	1
35	1	1
36	1	1
37	0	1
38	1	1

	0	1
0	1	1
1	1	1
2	1	1
3	0	0
4	1	1
5	1	0
6	1	1
7	1	1
8	1	1
9	1	0
10	1	1
11	1	1
12	1	0
13	1	1
14	1	1
15	1	1
16	1	1
17	1	1
18	1	1
19	1	1
20	1	1
21	0	1
22	1	1
23	1	1
24	0	0
25	1	1
26	1	1
27	1	1
28	1	1
29	1	1
30	0	0
31	0	0
32	0	0
33	1	1
34	1	1
35	1	1
36	1	1
37	0	1
38	1	1